Evidence map›Paper›PMID 42482943›Full record

ArticleFrontiers in surgery2026

Single-port robotic segmentectomy using the da Vinci SP system for non-small cell lung cancer.

Jun Hee Lee, Hyeong Hun Song, Byung Mo Gu, Soon Young Hwang, Kook Nam Han, Hyun Koo Kim

Abstract read
In one paragraph

Article in Frontiers in surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Jun Hee Lee *Department of Thoracic and Cardiovascular Surgery, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea.
Hyeong Hun Song *Department of Medicine, Korea University College of Medicine, Seoul, Republic of Korea.
Byung Mo GuDepartment of Thoracic and Cardiovascular Surgery, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea.
Soon Young HwangDepartment of Biostatistics, Korea University College of Medicine, Seoul, Republic of Korea.
Kook Nam HanDepartment of Thoracic and Cardiovascular Surgery, Chung-Ang University Gwangmyeong Hospital, Gwangmyeong, Republic of Korea.
Hyun Koo KimDepartment of Thoracic and Cardiovascular Surgery, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: Robot-assisted thoracic surgery using the single-port robotic system is a novel minimally invasive approach; however, clinical data on its use for anatomical segmentectomy remain limited. Therefore, this single-center retrospective cohort study aimed to investigate the safety and feasibility of this technique for segmentectomy by comparing its perioperative outcomes with those of multi-port robot-assisted thoracic surgery and video-assisted thoracoscopic surgery. Methods: Data from patients with non-small cell lung cancer who underwent anatomical segmentectomy from April 2014 to April 2025 were analyzed. Patients were categorized into single-port and multi-port robot-assisted thoracic surgery groups and video-assisted thoracoscopic surgery group according to the surgical approach. Perioperative outcomes were analyzed after propensity score matching. Results: A total of 345 patients were included in the analysis: single-port robot-assisted thoracic surgery (SP-RATS, Conclusions: Robotic segmentectomy using the da Vinci SP system appears safe and feasible, with favorable short-term perioperative outcomes. Large-scale, well-designed prospective trials with long-term follow-up are remained warranted to validate its clinical efficacy.

Indexed as

roboticssegmentectomysingle-portsingle-port robotic systemuniportal

Identifiers

PMID42482943
PMCPMC13386685

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.